Load in packages which are needed

Exercise 1

Use library(ggplot2) to load the package.

library(ggplot2)

Specify the dataset.

The data-set being used is ‘diamonds’ data-set Can find out more info using ?diamonds function

What does ggplot(diamonds) do?

ggplot(diamonds)

Answer: creates a grey block, this is because we haven’t said what we want on the x axis or y axis, or any other information

Add the aesthetics.

What does ggplot(diamonds, aes(x = carat, y = price)) do?

ggplot(diamonds, aes(x = carat, y = price))

Answer: this command defines the x and y axis. Note that the range is not random (it is based on the points in the data-set). However, we haven’t said how we want our datat to be plotted - we will add this now.

Add geometric objects

Add data points showing carat on the x-axis and price on the y-axis.

ggplot(diamonds, aes(x = carat, y = price)) +
  geom_point()

Color data points by cut.

ggplot(diamonds, aes(x = carat, y = price, colour=cut))+
  geom_point()

This has created a plot in which points are coloured by cut

Add a smoothed mean trend line.

ggplot(diamonds, aes(x = carat, y = price, colour=cut))+
  geom_point() +
  geom_smooth()

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

geom_smooth has added a trendline

Assign that last plot to an object called obds_diamonds.

obds_diamonds <- ggplot(diamonds, aes(x = carat, y = price, colour=cut))+
  geom_point() +
  geom_smooth()

print(obds_diamonds)

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Exercise 2

Predict the difference between these two plots

Plot 1

ggplot(diamonds, aes(x = carat, y = price, colour = cut)) +
  geom_point() +
  geom_smooth()

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Plot 2

ggplot(diamonds, aes(x = carat, y = price)) +
  geom_point(aes(colour = cut)) +
  geom_smooth()

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Answer: Plot 2 has only one trendline, whereas plot 1 has several trendlines for each different cut. This is because colour=cut was included in the ggplot() command for plot 1, but not for plot 2

Exercise 3

Trend lines

Using the ChickWeight data set:

Create a scatter plot of weight (y-axis) over time (x-axis).

ggplot(ChickWeight, aes(x=Time, y=weight)) +
         geom_point()

Color by diet. (Copy-paste and extend the code chunk above.)

ggplot(ChickWeight, aes(x=Time, y=weight, colour=Diet))+
  geom_point()

Add a linear mean trend line for each diet. (Copy-paste and extend the code chunk above.)

ggplot(ChickWeight, aes(x=Time, y=weight, colour=Diet))+
  geom_point()+
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

At this point you should be able to visually identify the diet that leads to the highest mean increase in weight.

Answer: diet 3 leads to the highest increase as this trendline is above the rest

Facet a ribbon of sub-plots, one per diet. (Copy-paste and extend the code chunk above.)

ggplot(ChickWeight, aes(x=Time, y=weight, colour=Diet))+
  geom_point()+
  geom_smooth(method="lm")+
  facet_wrap(~Diet, nrow=2)

## `geom_smooth()` using formula 'y ~ x'

Assign that last plot to an object called obds_chickweight.

obds_chickweight <- ggplot(ChickWeight, aes(x=Time, y=weight, colour=Diet))+
  geom_point()+
  geom_smooth(method="lm")+
  facet_wrap(~Diet, nrow=2)

Exercise 4

Bar plot

Load the ggplot2::msleep data set.

ggplot2::msleep

## # A tibble: 83 × 11
##    name   genus vore  order conservation sleep_total sleep_rem sleep_cycle awake
##    <chr>  <chr> <chr> <chr> <chr>              <dbl>     <dbl>       <dbl> <dbl>
##  1 Cheet… Acin… carni Carn… lc                  12.1      NA        NA      11.9
##  2 Owl m… Aotus omni  Prim… <NA>                17         1.8      NA       7  
##  3 Mount… Aplo… herbi Rode… nt                  14.4       2.4      NA       9.6
##  4 Great… Blar… omni  Sori… lc                  14.9       2.3       0.133   9.1
##  5 Cow    Bos   herbi Arti… domesticated         4         0.7       0.667  20  
##  6 Three… Brad… herbi Pilo… <NA>                14.4       2.2       0.767   9.6
##  7 North… Call… carni Carn… vu                   8.7       1.4       0.383  15.3
##  8 Vespe… Calo… <NA>  Rode… <NA>                 7        NA        NA      17  
##  9 Dog    Canis carni Carn… domesticated        10.1       2.9       0.333  13.9
## 10 Roe d… Capr… herbi Arti… lc                   3        NA        NA      21  
## # … with 73 more rows, and 2 more variables: brainwt <dbl>, bodywt <dbl>

Type the name of the data-set to see a bit more about it

Draw a bar plot of number of observations (i.e., rows) for each taxonomic order (i.e, one plot and one bar per taxonomic order).

ggplot(ggplot2::msleep, aes(x=order)) +
  geom_bar()

Note: must use geom_bar, not geom_histogram, as the data is not continuous

Change the angle and font size of the text for the x-axis ticks (not the axis titles). Justify the text of those x-axis ticks as right-aligned. (Copy-paste and extend the code chunk above.)

ggplot(ggplot2::msleep, aes(x=order)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle=45, size=9, hjust = 1))

Use theme to change aspects of x axis text axis.text.x must be specified to ensure that only the x axis text is affected element_text() is used to define angle, size, and hjust (justification, 1=right justified)

Change the value and font size of the title for both x and y axes.

ggplot(ggplot2::msleep, aes(x=order)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle=45, size=9, hjust = 1)) +
  labs(x="Taxonomic Order", y = "Number of Animals", title = "msleep dataset analysis")

Using theme can change the font size etc. for the axis titles, but labs must be used to change the words of the titles

Fill each bar with colors, proportionally to the count of each genus. (Copy-paste and extend the code chunk above.)

From this point onwards, you may need to iteratively resize the text of the ticks and axes for readability.

ggplot(ggplot2::msleep, aes(x=order, fill = genus)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle=45, size=9, hjust = 1)) +
  labs(x="Taxonomic Order", y = "Number of Animals", title = "msleep dataset analysis")

Adding fill=genus will colour the bars by genus, but makes the legend very large - need to fix this

Reduce the legend key size. (Recommendation: use unit(2, "mm")). (Copy-paste and extend the code chunk above.)

ggplot(ggplot2::msleep, aes(x=order, fill = genus)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle=45, size=9, hjust = 1), legend.key.size = unit(2, "mm")) +
  labs(x="Taxonomic Order", y = "Number of Animals", title = "msleep dataset analysis")

Using the command legend.key.size we can alter the size of each key in the legend - by setting unit(2,“mm”) we have reduced the size

Force the legend to be display in 3 columns. (Recommendation: use guide_legend(...)). (Copy-paste and extend the code chunk above.)

ggplot(ggplot2::msleep, aes(x=order, fill = genus)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle=45, size=9, hjust = 1), legend.key.size = unit(2, "mm")) +
  labs(x="Taxonomic Order", y = "Number of Animals", title = "msleep dataset analysis") +
  guides(fill=guide_legend(ncol=3))

Using the guides function, we set the number of columns in the legend to 3

Add a contour of thin black lines to the bars.

ggplot(ggplot2::msleep, aes(x=order, fill = genus)) +
  geom_bar(colour="black", size=0.1) +
  theme(axis.text.x = element_text(angle=45, size=9, hjust = 1), legend.key.size = unit(2, "mm")) +
  labs(x="Taxonomic Order", y = "Number of Animals", title = "msleep dataset analysis") +
  guides(fill=guide_legend(ncol=3))

Adding colour=“black” into the geom_bar function allows us to put a bar around each genus for clarity. Adding size=0.1 allows us to reduce the size of the line so that it is not too thick (default =1)

Assign that last plot to an object called obds_msleep.

obds_sleep <- ggplot(ggplot2::msleep, aes(x=order, fill = genus)) +
  geom_bar(colour="black", size=0.1) +
  theme(axis.text.x = element_text(angle=45, size=9, hjust = 1), legend.key.size = unit(2, "mm")) +
  labs(x="Taxonomic Order", y = "Number of Animals", title = "msleep dataset analysis") +
  guides(fill=guide_legend(ncol=3))

Exercise 5

Plotting grids

Collate the plots that we assigend to objects through the day, as a single plot.
- Plots: obds_diamonds, obds_chickweight, obds_msleep.
- Methods: cowplot::plot_grid(), patchwork, gridExtra::grid.arrange().

Using `cowplot`.

Save each plot as its own row Then use cowplot to combine each row into one plot

library(cowplot)

first_row <- cowplot::plot_grid(obds_diamonds, labels="diamonds", ncol=1, nrow=1)

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

second_row <- cowplot::plot_grid(obds_chickweight, labels="chickweight", ncol=1, nrow=1)

## `geom_smooth()` using formula 'y ~ x'

third_row <- cowplot::plot_grid(obds_sleep, labels="sleep", ncol=1, nrow=1)
superplot<-plot_grid(first_row, second_row, third_row, ncol=1, nrow=3)

superplot

Using `patchwork`.

Use / to place one plot on top of another plot Use + to place one plot next to another plot

library(patchwork)

patchwork_plot <- obds_chickweight/obds_diamonds/obds_sleep
patchwork_plot

## `geom_smooth()` using formula 'y ~ x'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Using `gridExtra`.

Similar to using cowplot

library(gridExtra)

gridExtra_plot <- gridExtra::grid.arrange(obds_chickweight, obds_diamonds, obds_sleep, ncol=1, nrow=3)

## `geom_smooth()` using formula 'y ~ x'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

gridExtra_plot

## TableGrob (3 x 1) "arrange": 3 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (2-2,1-1) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]

We can also try a different way of using the gridExtra software to get a different arrangement of graphs in the file

library(gridExtra)

layout <- rbind(c(1,2), c(3,3))

gridExtra_plot_2 <- gridExtra::grid.arrange(obds_diamonds,obds_chickweight, obds_sleep, layout_matrix=layout)

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using formula 'y ~ x'

Exporting plots

Export the new plot in a PDF file, and open it in a PDF viewer (e.g. Adobe Acrobat Reader DC).

You will likely need a few attempts to fine-tune the width and height of the output file.

We can use ggsave to save the various plots that we have created We give the plot name, the file extension, and define the width/height

ggsave("super_plot.pdf", plot=superplot, width=10, height=15)

ggsave("patchwork_plot.pdf", plot=patchwork_plot, width=10, height=15)

## `geom_smooth()` using formula 'y ~ x'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

ggsave("gridExtra_plot.pdf", plot=gridExtra_plot, width=10, height=15)

Exercise 6

Pair programming

Explore the data set ggplot2::mpg and generate the most informative plot that you can!

geom_abline is used to draw a line on the plots this is unrelated to the data, but just draws a slope - can be used for comparison) we have also added linetype (dashed) and size specifications in the function to define these aspects

panel.grid.minor=element_blank is being used to remove the minor grid lines - this can help with clarity

facet_wrap function is needed to draw an individual plot for each manufacturer

labs has been used to add more informative x and y labels to the plot

strip.text=element_text in the theme function allows us to define the size of the text above each plot (i.e. the car manufacturer name)

ggplot(ggplot2::mpg, aes(x=cty, y=hwy, colour=year)) +
  geom_point(size=0.5) +
  geom_abline(intercept=0, slope=1, size=0.1, linetype="dashed")+
  facet_wrap(~ manufacturer) +
  theme(panel.grid.minor=element_blank(), strip.text=element_text(size=10)) +
  labs(y="Highway petrol useage", x="City petrol useage")

ggplot_example_code

Hannah Pook

2022-05-12

Exercise 1

Specify the dataset.

Add the aesthetics.

Add geometric objects

Exercise 2

Plot 1

Plot 2

Exercise 3

Trend lines

Exercise 4

Bar plot

Exercise 5

Plotting grids

Using `cowplot`.

Using `patchwork`.

Using `gridExtra`.

Exporting plots

Exercise 6

Pair programming

ggplot_example_code

Hannah Pook

2022-05-12

Exercise 1

Specify the dataset.

Add the aesthetics.

Add geometric objects

Exercise 2

Plot 1

Plot 2

Exercise 3

Trend lines

Exercise 4

Bar plot

Exercise 5

Plotting grids

Using cowplot.

Using patchwork.

Using gridExtra.

Exporting plots

Exercise 6

Pair programming

Using `cowplot`.

Using `patchwork`.

Using `gridExtra`.